{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聚类案例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "6             Augsberger       175      24      5.5  0.40\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "beer = pd.read_csv('data.txt', sep=' ')\n",
    "beer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = beer[['calories','sodium','alcohol','cost']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=3).fit(X)  # n_clusters簇，聚成多少个堆\n",
    "km2 = KMeans(n_clusters=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 2, 0, 0, 2, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2\n",
       "0              Budweiser       144      15      4.7  0.43        0         1\n",
       "1                Schlitz       151      19      4.9  0.43        0         1\n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1\n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1\n",
       "4               Heineken       152      11      5.0  0.77        0         1\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1\n",
       "6             Augsberger       175      24      5.5  0.40        0         1\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1\n",
       "16                 Hamms       139      19      4.4  0.43        0         1\n",
       "10                 Coors       140      18      4.6  0.44        0         1\n",
       "14                 Kirin       149       6      5.0  0.79        0         1\n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1\n",
       "13                 Becks       150      19      4.7  0.76        0         1\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0\n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0\n",
       "11           Coors_Light       102      15      4.1  0.46        1         0\n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster'] = km.labels_\n",
    "beer['cluster2'] = km2.labels_\n",
    "beer.sort_values('cluster')  # 查看聚类的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "%matplotlib inline\n",
    "\n",
    "cluster_centers = km.cluster_centers_\n",
    "cluster_centers_2 = km2.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories  sodium   alcohol      cost  cluster2\n",
       "cluster                                                \n",
       "0          150.00    17.0  4.521429  0.520714         1\n",
       "1          102.75    10.0  4.075000  0.440000         0\n",
       "2           70.00    10.5  2.600000  0.420000         0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby('cluster').mean()  # 查看每个cluster在每个特征的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.833333</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            calories     sodium   alcohol      cost   cluster\n",
       "cluster2                                                     \n",
       "0          91.833333  10.166667  3.583333  0.433333  1.333333\n",
       "1         150.000000  17.000000  4.521429  0.520714  0.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby('cluster2').mean()  # 查看每个cluster在每个特征的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = beer.groupby('cluster').mean().reset_index()  # 获取中心点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 14 # 指定画布大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "colors = np.array(['red','green','blue','yellow'])  # 指定颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Alcohol')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(beer['calories'], beer['alcohol'], c=colors[beer['cluster']])  # 画点\n",
    "\n",
    "plt.scatter(centers.calories, centers.alcohol,\n",
    "            linewidths=3,marker='+',s=300,c='black')  # 画中心点\n",
    "\n",
    "plt.xlabel('Calories')\n",
    "plt.ylabel('Alcohol')\n",
    "# 两个维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'With 3 centroids initialized')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画四个维度，三个簇的，两两指标比较\n",
    "scatter_matrix(beer[['calories','sodium','alcohol','cost']], \n",
    "               s=100,alpha=1,c=colors[beer['cluster']],figsize=(10,10))\n",
    "\n",
    "plt.suptitle('With 3 centroids initialized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'With 2 centroids initialized')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画四个维度，两个簇的，两两指标比较\n",
    "scatter_matrix(beer[['calories','sodium','alcohol','cost']], \n",
    "               s=100,alpha=1,c=colors[beer['cluster2']],figsize=(10,10))\n",
    "\n",
    "plt.suptitle('With 2 centroids initialized')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样我们就可以观测，到底选择3个簇还是2个簇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaled data 查看标准化后的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38791334,  0.00779468,  0.43380786, -0.45682969],\n",
       "       [ 0.6250656 ,  0.63136906,  0.62241997, -0.45682969],\n",
       "       [ 0.82833896,  0.00779468, -3.14982226, -0.10269815],\n",
       "       [ 1.26876459, -1.23935408,  0.90533814,  1.66795955],\n",
       "       [ 0.65894449, -0.6157797 ,  0.71672602,  1.95126478],\n",
       "       [ 0.42179223,  1.25494344,  0.3395018 , -1.5192243 ],\n",
       "       [ 1.43815906,  1.41083704,  1.1882563 , -0.66930861],\n",
       "       [ 0.55730781,  1.87851782,  0.43380786, -0.52765599],\n",
       "       [-1.1366369 , -0.7716733 ,  0.05658363, -0.45682969],\n",
       "       [-0.66233238, -1.08346049, -0.5092527 , -0.66930861],\n",
       "       [ 0.25239776,  0.47547547,  0.3395018 , -0.38600338],\n",
       "       [-1.03500022,  0.00779468, -0.13202848, -0.24435076],\n",
       "       [ 0.08300329, -0.6157797 , -0.03772242,  0.03895447],\n",
       "       [ 0.59118671,  0.63136906,  0.43380786,  1.88043848],\n",
       "       [ 0.55730781, -1.39524768,  0.71672602,  2.0929174 ],\n",
       "       [-2.18688263,  0.00779468, -1.82953748, -0.81096123],\n",
       "       [ 0.21851887,  0.63136906,  0.15088969, -0.45682969],\n",
       "       [ 0.38791334,  1.41083704,  0.62241997, -0.45682969],\n",
       "       [-2.05136705, -1.39524768, -1.26370115, -0.24435076],\n",
       "       [-1.20439469, -1.23935408, -0.03772242, -0.17352445]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()  # 归一化\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3).fit(X_scaled)  # 重新训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "0              Budweiser       144      15      4.7  0.43        0         1   \n",
       "1                Schlitz       151      19      4.9  0.43        0         1   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1   \n",
       "16                 Hamms       139      19      4.4  0.43        0         1   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1   \n",
       "6             Augsberger       175      24      5.5  0.40        0         1   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1   \n",
       "10                 Coors       140      18      4.6  0.44        0         1   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0   \n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1   \n",
       "11           Coors_Light       102      15      4.1  0.46        1         0   \n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0   \n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0   \n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0   \n",
       "13                 Becks       150      19      4.7  0.76        0         1   \n",
       "14                 Kirin       149       6      5.0  0.79        0         1   \n",
       "4               Heineken       152      11      5.0  0.77        0         1   \n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1   \n",
       "\n",
       "    scaled_cluster  \n",
       "0                0  \n",
       "1                0  \n",
       "17               0  \n",
       "16               0  \n",
       "5                0  \n",
       "6                0  \n",
       "7                0  \n",
       "10               0  \n",
       "15               1  \n",
       "12               1  \n",
       "11               1  \n",
       "9                1  \n",
       "8                1  \n",
       "2                1  \n",
       "18               1  \n",
       "19               1  \n",
       "13               2  \n",
       "14               2  \n",
       "4                2  \n",
       "3                2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['scaled_cluster'] = km.labels_\n",
    "beer.sort_values('scaled_cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.375</td>\n",
       "      <td>21.125</td>\n",
       "      <td>4.7875</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.375</td>\n",
       "      <td>10.875</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>0.4475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155.250</td>\n",
       "      <td>10.750</td>\n",
       "      <td>4.9750</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                calories  sodium  alcohol    cost  cluster  cluster2\n",
       "scaled_cluster                                                      \n",
       "0                148.375  21.125   4.7875  0.4075      0.0      1.00\n",
       "1                105.375  10.875   3.3250  0.4475      1.0      0.25\n",
       "2                155.250  10.750   4.9750  0.7625      0.0      1.00"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby('scaled_cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A985DF28>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A97A41D0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000297A98DFEF0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A98F0A20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9EDBEF0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9C860B8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9C860F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9B51320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297AA04B470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9FAF5C0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000297AA08F710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9EEA860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9F3E9B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000297A9F9DB00>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(beer[['calories','sodium','alcohol','cost']], \n",
    "               s=100,alpha=1,c=colors[beer['scaled_cluster']],figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聚类评估：轮廓系数（Silhouette Coefficient）\n",
    "最常用的评估方法\n",
    "<img src=\"assets/20201125210741.png\" width=\"50%\">\n",
    "<ul>\n",
    "    <li>计算样本到同簇其他样本的平均距离ai，ai越小,说明样本越应该被聚类到该簇。将ai称为样本的簇内不相似度。\n",
    "    <li>计算样本到其他某族Cj的所有样本的平均距离bij,称为样本i与Cj的不相似度。定义为样本的族间不相似度:bi=min{bi1, bi2,..., bik}\n",
    "    <li>Si接近1,则说明样本聚类合理\n",
    "    <li>Si接近-1,则说明样本更应该分类到另外的簇\n",
    "    <li>若si近似为0,则说明样本在两个簇的边界上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1797806808940007 0.6731775046455796\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score_scaled = metrics.silhouette_score(X,beer.scaled_cluster)  # 归一化的聚类结果\n",
    "score = metrics.silhouette_score(X,beer.cluster)  # 非归一化的聚类结果\n",
    "print(score_scaled, score)  # 不做归一化反而好了，所以不一定要做归一化，主要看结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 0.6917656034079486,\n",
       " 3: 0.6731775046455796,\n",
       " 4: 0.5857040721127795,\n",
       " 5: 0.422548733517202,\n",
       " 6: 0.4559182167013377,\n",
       " 7: 0.43776116697963124,\n",
       " 8: 0.38946337473125997,\n",
       " 9: 0.39746405172426014,\n",
       " 10: 0.3915697409245163,\n",
       " 11: 0.32472080133848924,\n",
       " 12: 0.377361166112964,\n",
       " 13: 0.31221439248428434,\n",
       " 14: 0.30707782144770296,\n",
       " 15: 0.31834561839139497,\n",
       " 16: 0.2849514001174898,\n",
       " 17: 0.23498077333071996,\n",
       " 18: 0.1588091017496281,\n",
       " 19: 0.08423051380151177}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []  # 遍历获取最佳K值，选取score最大的k值\n",
    "scores_dict = {}\n",
    "for k in range(2,20):\n",
    "    labels = KMeans(n_clusters=k).fit(X).labels_\n",
    "    score = metrics.silhouette_score(X, labels)\n",
    "    scores_dict[k] = score\n",
    "    scores.append(score)\n",
    "\n",
    "scores_dict  # 可以看到k=2时，score最大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x297aa2e8da0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(2,20)),scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "db = DBSCAN(eps=10, min_samples=2).fit(X)  # eps半径,min_samples最小密度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th>cluster_db</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0   \n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1   \n",
       "6             Augsberger       175      24      5.5  0.40        0         1   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1   \n",
       "16                 Hamms       139      19      4.4  0.43        0         1   \n",
       "14                 Kirin       149       6      5.0  0.79        0         1   \n",
       "13                 Becks       150      19      4.7  0.76        0         1   \n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1   \n",
       "10                 Coors       140      18      4.6  0.44        0         1   \n",
       "0              Budweiser       144      15      4.7  0.43        0         1   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1   \n",
       "4               Heineken       152      11      5.0  0.77        0         1   \n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1   \n",
       "1                Schlitz       151      19      4.9  0.43        0         1   \n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0   \n",
       "11           Coors_Light       102      15      4.1  0.46        1         0   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0   \n",
       "\n",
       "    scaled_cluster  cluster_db  \n",
       "9                1          -1  \n",
       "3                2          -1  \n",
       "6                0          -1  \n",
       "17               0           0  \n",
       "16               0           0  \n",
       "14               2           0  \n",
       "13               2           0  \n",
       "12               1           0  \n",
       "10               0           0  \n",
       "0                0           0  \n",
       "7                0           0  \n",
       "5                0           0  \n",
       "4                2           0  \n",
       "2                1           0  \n",
       "1                0           0  \n",
       "8                1           1  \n",
       "11               1           1  \n",
       "19               1           1  \n",
       "15               1           2  \n",
       "18               1           2  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster_db'] = labels\n",
    "beer.sort_values('cluster_db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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